Pular para o conteúdo principal

Grafos/DadosConectados na Visão do Gartner

Gartner Identifies Top 10 Data and Analytics Technology Trends for 2019

 Trend No. 5: Graph

Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions.

The application of graph processing and graph DBMSs will grow at 100 percent annually through 2022 to continuously accelerate data preparation and enable more complex and adaptive data science. 

Graph data stores can efficiently model, explore and query data with complex interrelationships across data silos, but the need for specialized skills has limited their adoption to date, according to Gartner.

Graph analytics will grow in the next few years due to the need to ask complex questions across complex data, which is not always practical or even possible at scale using SQL queries.

 

Gartner Identifies Top 10 Data and Analytics Technology Trends for 2020

Trend 4: X Analytics

Gartner coined the term “X analytics” to be an umbrella term, where X is the data variable for a range of different structured and unstructured content such as text analytics, video analytics, audio analytics, etc.

During the COVID-19 pandemic, AI has been critical in combing through thousands of research papers, news sources, social media posts and clinical trials data to help medical and public health experts predict disease spread, capacity-plan, find new treatments and identify vulnerable populations. X analytics combined with AI and other techniques such as graph analytics will play a key role in identifying, predicting and planning for natural disasters and other crises in the future.

Trend 10: Relationships Form the Foundation of Data and Analytics Value

By 2023, graph technologies will facilitate rapid contextualization for decision making in 30% of organizations worldwide. Graph analytics is a set of analytic techniques that allows for the exploration of relationships between entities of interest such as organizations, people and transactions. It helps data and analytics leaders find unknown relationships in data and review data not easily analyzed with traditional analytics.


 


 

 

Comentários

  1. Veronica dos Santos29 de abril de 2021 às 12:54

    O Gartner faz anualmente análises de mercado em determinados segmentos. Em 2019 e 2020, grafos de conhecimento estão em evidência.

    ResponderExcluir

Postar um comentário

Sinta-se a vontade para comentar. Críticas construtivas são sempre bem vindas.

Postagens mais visitadas deste blog

Connected Papers: Uma abordagem alternativa para revisão da literatura

Durante um projeto de pesquisa podemos encontrar um artigo que nos identificamos em termos de problema de pesquisa e também de solução. Então surge a vontade de saber como essa área de pesquisa se desenvolveu até chegar a esse ponto ou quais desdobramentos ocorreram a partir dessa solução proposta para identificar o estado da arte nesse tema. Podemos seguir duas abordagens:  realizar uma revisão sistemática usando palavras chaves que melhor caracterizam o tema em bibliotecas digitais de referência para encontrar artigos relacionados ou realizar snowballing ancorado nesse artigo que identificamos previamente, explorando os artigos citados (backward) ou os artigos que o citam (forward)  Mas a ferramenta Connected Papers propõe uma abordagem alternativa para essa busca. O problema inicial é dado um artigo de interesse, precisamos encontrar outros artigos relacionados de "certa forma". Find different methods and approaches to the same subject Track down the state of the art rese...
3 comentários
Leia mais

Knowledge Graph Embedding with Triple Context - Leitura de Abstract

  Jun Shi, Huan Gao, Guilin Qi, and Zhangquan Zhou. 2017. Knowledge Graph Embedding with Triple Context. In Proceedings of the 2017 ACM on Conference on Information and Knowledge Management (CIKM '17). Association for Computing Machinery, New York, NY, USA, 2299–2302. https://doi.org/10.1145/3132847.3133119 ABSTRACT Knowledge graph embedding, which aims to represent entities and relations in vector spaces, has shown outstanding performance on a few knowledge graph completion tasks. Most existing methods are based on the assumption that a knowledge graph is a set of separate triples, ignoring rich graph features, i.e., structural information in the graph. In this paper, we take advantages of structures in knowledge graphs, especially local structures around a triple, which we refer to as triple context. We then propose a Triple-Context-based knowledge Embedding model (TCE). For each triple, two kinds of structure information are considered as its context in the graph; one is the out...
Postar um comentário
Leia mais

Exploratory Search: From Finding to Understanding - Leitura de Artigo

Gary Marchionini. 2006. Exploratory search: from finding to understanding. Commun. ACM  49, 4 (April 2006), 41–46. https://doi.org/10.1145/1121949.1121979   This article distinguishes exploratory search that blends quer ying and browsing strategies from retrieval that is best served by analytical strategies ...   Exploratory search. Search is a fundamental life activity.   A hierarchy of information needs may also be defined that ranges from basic facts that guide short-term actions (for example, the predicted chance for rain today to decide whether to bring an umbr ella) to networks of related concepts that help us under stand phenomena or execute complex activities (for example, the relationships between bond prices and stock prices to manage a retirement portfolio) to com plex networks of tacit and explicit knowledge that accretes as expertise over a lifetime (for example, the most promising paths of investigation for the sea soned scholar or designer)....
2 comentários
Leia mais